Machine Learning for All

  • 4.7
Approx. 22 hours to complete

Course Summary

This course provides a comprehensive introduction to machine learning, covering the basic concepts and techniques in a hands-on manner. Students will learn how to implement algorithms and evaluate performance using real-world datasets.

Key Learning Points

  • Learn the fundamentals of machine learning, including supervised and unsupervised learning, regression, clustering, and classification
  • Gain practical experience by working with real-world datasets and implementing algorithms in Python
  • Understand how to evaluate model performance and optimize algorithms for better results

Job Positions & Salaries of people who have taken this course might have

    • USA: $112,000
    • India: ₹1,000,000
    • Spain: €45,000
    • USA: $112,000
    • India: ₹1,000,000
    • Spain: €45,000

    • USA: $117,000
    • India: ₹1,200,000
    • Spain: €50,000
    • USA: $112,000
    • India: ₹1,000,000
    • Spain: €45,000

    • USA: $117,000
    • India: ₹1,200,000
    • Spain: €50,000

    • USA: $124,000
    • India: ₹1,500,000
    • Spain: €55,000

Related Topics for further study


Learning Outcomes

  • Implement machine learning algorithms in Python
  • Evaluate model performance and optimize algorithms
  • Apply machine learning techniques to real-world datasets

Prerequisites or good to have knowledge before taking this course

  • Basic knowledge of Python programming
  • Understanding of basic statistics and linear algebra

Course Difficulty Level

Beginner

Course Format

  • Online
  • Self-paced

Similar Courses

  • Applied Data Science with Python
  • Data Mining and Machine Learning
  • Introduction to Deep Learning

Related Education Paths


Notable People in This Field

  • Andrew Ng
  • Yann LeCun
  • Fei-Fei Li

Related Books

Description

Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it. Machine Learning has a reputation for being one of the most complex areas of computer science, requiring advanced mathematics and engineering skills to understand it. While it is true that working as a Machine Learning engineer does involve a lot of mathematics and programming, we believe that anyone can understand the basic concepts of Machine Learning, and given the importance of this technology, everyone should. The big AI breakthroughs sound like science fiction, but they come down to a simple idea: the use of data to train statistical algorithms. In this course you will learn to understand the basic idea of machine learning, even if you don't have any background in math or programming. Not only that, you will get hands on and use user friendly tools developed at Goldsmiths, University of London to actually do a machine learning project: training a computer to recognise images. This course is for a lot of different people. It could be a good first step into a technical career in Machine Learning, after all it is always better to start with the high level concepts before the technical details, but it is also great if your role is non-technical. You might be a manager or other non-technical role in a company that is considering using Machine Learning. You really need to understand this technology, and this course is a great place to get that understanding. Or you might just be following the news reports about AI and interested in finding out more about the hottest new technology of the moment. Whoever you are, we are looking forward to guiding you through you first machine learning project.

Knowledge

  • Y​ou will understand the basic of how modern machine learning technologies work
  • Y​ou will be able to explain and predict how data affects the results of machine learning
  • Y​ou will be able to use a non-programming based platform train a machine learning module using a dataset
  • Y​ou will be able to form an informed opinion on the benefits and dangers of machine learning to society

Outline

  • Machine learning
  • Introduction: Computers that see
  • Artificial intelligence
  • Machine learning
  • Machine learning algorithms
  • Interview with Machine Learning Experts
  • Summary
  • Welcome to Machine Learning for All
  • Machine learning exercise
  • Computers that see
  • Alpha Go
  • Machine Learning Summative Quiz
  • Data Features
  • The bit
  • Bytes and numbers
  • Other types of data
  • Introduction to Data Features
  • Data features
  • Neural networks
  • Interview: Data Features
  • Bag of Words
  • Data Features Summative Quiz
  • Machine Learning in Practice
  • Introduction to Machine Learning in practice
  • Testing
  • Problems with machine learning
  • Applications of machine learning
  • Dangers of machine learning
  • Interview: Benefits and Dangers of Machine Learning
  • Trying different datasets
  • Evaluating the four datasets
  • Further Reading
  • Machine Learning in Practice Summative Quiz
  • Your Machine Learning Project
  • Introduction: Collecting your own dataset
  • Collecting a dataset
  • Interview: Advice for your first Machine Learning Project
  • Summary
  • Collecting a dataset
  • What's next?
  • Preparing for your machine learning project

Summary of User Reviews

Discover the world of machine learning with the University of London! This course is highly recommended by many users due to its comprehensive approach to teaching and the practical application of the concepts learned. Learn at your own pace and apply your newfound knowledge to real-world problems.

Key Aspect Users Liked About This Course

The practical application of concepts learned

Pros from User Reviews

  • Comprehensive approach to teaching
  • Real-world examples and applications
  • Flexible learning schedule
  • Engaging and knowledgeable instructors
  • Great value for the price

Cons from User Reviews

  • Some users found the pace too fast
  • Requires a basic understanding of mathematics and programming
  • Not suitable for advanced machine learning practitioners
  • Some technical issues with the platform
  • Limited interaction with other students
English
Available now
Approx. 22 hours to complete
Dr Marco Gillies
University of London
Coursera

Instructor

Dr Marco Gillies

  • 4.7 Raiting
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